Gaussian Processes for Machine Learning (GPML) Toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace's method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

Authors

• Carl Edward Rasmussen
• Hannes Nickisch

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